



def       lowerCAmelCase_	(    _lowercase							:  int		,  _lowercase							:  int)				->			Optional[Any]:







			"""simple docstring"""
			return abs(lowerCAmelCase__) if a == 0 else greatest_common_divisor(b % a		,  lowerCAmelCase__)

def       lowerCAmelCase_	(    _lowercase							:  int		,  _lowercase							:  int)				->			str:







			"""simple docstring"""


			while y:  # --> when y=0 then loop will terminate and return x as final GCD.
						a__  :		Optional[Any]					=    y, x % y
			return abs(lowerCAmelCase__)

def       lowerCAmelCase_	(    )				->			Optional[Any]:







			"""simple docstring"""


			try:
						a__  :		List[Any]					=    input("""Enter two integers separated by comma (,): """).split(""",""")
						a__  :		Union[str, Any]					=    int(nums[0])
						a__  :		Dict					=    int(nums[1])
						print(
						    F'''greatest_common_divisor({num_a}, {num_a}) = '''
						    F'''{greatest_common_divisor(lowerCAmelCase__		,  lowerCAmelCase__)}''')
						print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCAmelCase__		,  lowerCAmelCase__)}''')
			except (IndexError, UnboundLocalError, ValueError):
						print("""Wrong input""")


if __name__ == "__main__":
		main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






